[R-meta] F test vs QM test for test of moderators

[Yefeng Yang]

Dear Huang

If you understand what QM test is, it is not difficult to find out "Test of Moderators (coefficients 2:3)" printed below your output is the QM test results. Your test statistic was tested against F distribution. You can also use chi-square distribution (but not recommended). In essence, QM test is a sort of omnibus test or joint null-hypothesis test.

Best,
Yefeng


________________________________
From: R-sig-meta-analysis <r-sig-meta-analysis-bounces using r-project.org> on behalf of Huang Wu via R-sig-meta-analysis <r-sig-meta-analysis using r-project.org>
Sent: Saturday, 29 April 2023 12:34
To: R meta <r-sig-meta-analysis using r-project.org>
Cc: Huang Wu <wuhuang0421 using gmail.com>
Subject: [R-meta] F test vs QM test for test of moderators

Dear all,

I am writing to ask about the Test of Moderators in meta-analysis.
Specifically, I am curious about the appropriate test to use between the F
test and QM test. I ran the following code and obtained results using the F
test for the Test of Moderators. However, I would like to explore how to
obtain QM test results.

Could you kindly advise me on the suitable test to use and how to obtain QM
test results using the metafor package?

Thank you for your assistance.
Huang
----------------------------------------------------

USnew_c.Dnoctl.model <- rma.mv(yi=effect_d, #effect size
                               V = VUSnew_c.Dnoctl, #variance (tHIS IS WHAt
CHANGES FROM HEmodel)
                               mods = ~ grade_level,
                               random = ~1 | ID/eid, #nesting structure
                               test= "t", #use t-tests
                               data=USnew_c.Dnoctl, #define data
                               method="REML") #estimate variances using REML

summary(USnew_c.Dnoctl.model)

----------------------------------------------------
Multivariate Meta-Analysis Model (k = 142; method: REML)

  logLik  Deviance       AIC       BIC      AICc
 16.9680  -33.9361  -23.9361   -9.2637  -23.4849

Variance Components:

            estim    sqrt  nlvls  fixed  factor
sigma^2.1  0.0300  0.1733     19     no      ID
sigma^2.2  0.0161  0.1270    142     no  ID/eid

Test for Residual Heterogeneity:
QE(df = 139) = 344.3018, p-val < .0001

Test of Moderators (coefficients 2:3):
F(df1 = 2, df2 = 139) = 1.1327, p-val = 0.3251

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